Zobrazeno 1 - 10
of 27
pro vyhledávání: '"Li, Jinyu"'
Autor:
Wang, Xiaofei, Eskimez, Sefik Emre, Thakker, Manthan, Yang, Hemin, Zhu, Zirun, Tang, Min, Xia, Yufei, Li, Jinzhu, Zhao, Sheng, Li, Jinyu, Kanda, Naoyuki
Recently, zero-shot text-to-speech (TTS) systems, capable of synthesizing any speaker's voice from a short audio prompt, have made rapid advancements. However, the quality of the generated speech significantly deteriorates when the audio prompt conta
Externí odkaz:
http://arxiv.org/abs/2406.05699
Autor:
Le, Chenyang, Qian, Yao, Wang, Dongmei, Zhou, Long, Liu, Shujie, Wang, Xiaofei, Yousefi, Midia, Qian, Yanmin, Li, Jinyu, Zhao, Sheng, Zeng, Michael
There is a rising interest and trend in research towards directly translating speech from one language to another, known as end-to-end speech-to-speech translation. However, most end-to-end models struggle to outperform cascade models, i.e., a pipeli
Externí odkaz:
http://arxiv.org/abs/2405.17809
Autor:
Zhang, Leying, Qian, Yao, Zhou, Long, Liu, Shujie, Wang, Dongmei, Wang, Xiaofei, Yousefi, Midia, Qian, Yanmin, Li, Jinyu, He, Lei, Zhao, Sheng, Zeng, Michael
Recent advancements in zero-shot text-to-speech (TTS) modeling have led to significant strides in generating high-fidelity and diverse speech. However, dialogue generation, along with achieving human-like naturalness in speech, continues to be a chal
Externí odkaz:
http://arxiv.org/abs/2404.06690
Autor:
Xin, Detai, Tan, Xu, Shen, Kai, Ju, Zeqian, Yang, Dongchao, Wang, Yuancheng, Takamichi, Shinnosuke, Saruwatari, Hiroshi, Liu, Shujie, Li, Jinyu, Zhao, Sheng
We present RALL-E, a robust language modeling method for text-to-speech (TTS) synthesis. While previous work based on large language models (LLMs) shows impressive performance on zero-shot TTS, such methods often suffer from poor robustness, such as
Externí odkaz:
http://arxiv.org/abs/2404.03204
Autor:
Hu, Shujie, Zhou, Long, Liu, Shujie, Chen, Sanyuan, Hao, Hongkun, Pan, Jing, Liu, Xunying, Li, Jinyu, Sivasankaran, Sunit, Liu, Linquan, Wei, Furu
The recent advancements in large language models (LLMs) have revolutionized the field of natural language processing, progressively broadening their scope to multimodal perception and generation. However, effectively integrating listening capabilitie
Externí odkaz:
http://arxiv.org/abs/2404.00656
Autor:
Ju, Zeqian, Wang, Yuancheng, Shen, Kai, Tan, Xu, Xin, Detai, Yang, Dongchao, Liu, Yanqing, Leng, Yichong, Song, Kaitao, Tang, Siliang, Wu, Zhizheng, Qin, Tao, Li, Xiang-Yang, Ye, Wei, Zhang, Shikun, Bian, Jiang, He, Lei, Li, Jinyu, Zhao, Sheng
While recent large-scale text-to-speech (TTS) models have achieved significant progress, they still fall short in speech quality, similarity, and prosody. Considering speech intricately encompasses various attributes (e.g., content, prosody, timbre,
Externí odkaz:
http://arxiv.org/abs/2403.03100
We present a cost-effective method to integrate speech into a large language model (LLM), resulting in a Contextual Speech Model with Instruction-following/in-context-learning Capabilities (COSMIC) multi-modal LLM. Using GPT-3.5, we generate Speech C
Externí odkaz:
http://arxiv.org/abs/2311.02248
Autor:
Zhang, Ziqiang, Zhou, Long, Wang, Chengyi, Chen, Sanyuan, Wu, Yu, Liu, Shujie, Chen, Zhuo, Liu, Yanqing, Wang, Huaming, Li, Jinyu, He, Lei, Zhao, Sheng, Wei, Furu
We propose a cross-lingual neural codec language model, VALL-E X, for cross-lingual speech synthesis. Specifically, we extend VALL-E and train a multi-lingual conditional codec language model to predict the acoustic token sequences of the target lang
Externí odkaz:
http://arxiv.org/abs/2303.03926
Autor:
Zhu, Qiushi, Zhou, Long, Zhang, Ziqiang, Liu, Shujie, Jiao, Binxing, Zhang, Jie, Dai, Lirong, Jiang, Daxin, Li, Jinyu, Wei, Furu
Although speech is a simple and effective way for humans to communicate with the outside world, a more realistic speech interaction contains multimodal information, e.g., vision, text. How to design a unified framework to integrate different modal in
Externí odkaz:
http://arxiv.org/abs/2211.11275
Autor:
Gaur, Yashesh, Kibre, Nick, Xue, Jian, Shu, Kangyuan, Wang, Yuhui, Alphanso, Issac, Li, Jinyu, Gong, Yifan
Automatic Speech Recognition (ASR) systems typically yield output in lexical form. However, humans prefer a written form output. To bridge this gap, ASR systems usually employ Inverse Text Normalization (ITN). In previous works, Weighted Finite State
Externí odkaz:
http://arxiv.org/abs/2211.03721